Time data analysis

ABSTRACT

Methods, systems, and computer-readable media are provided for analyzing amounts of time spent in medical records. Time data may be analyzed by clinician, by specialty, by location, by patient, by activity, and the like. Such analysis will provide, among other things, an insight into how much total time is spent in medical records, how the time is spent, when the time is spent, etc. By breaking down the time data it may be possible to identify modifications to reduce the amount of time spent in medical records.

CROSS-REFERENCE TO RELATED APPLICATIONS AND PRIORITY CLAIM

This application is a continuation of U.S. patent application Ser. No.14/505,695, filed Dec. 3, 2014, which is hereby incorporated byreference in its entirety.

BACKGROUND

Clinicians access electronic medical records (EMRs) multiple timeswithin a given shift of work and even outside of their respective workshifts. The EMRs are accessed to, for example, document patient care,examine patient histories, review medications, prescribe medications,enter orders for a patient, check a status of an order, etc. While theEMR is necessary to provide efficient patient care, if not used properlyit may not yield a highest efficiency result or may even reduceefficiency. For example, a clinician that is not properly trained on anEMR system may not document into a chart as quickly as he/she should.Another example may be a department within a healthcare facility thatdoes not have customized software to meet their needs (e.g., anobstetric practice has specific timelines and milestones tracked thatare not relevant in, for instance, cardiology, and may requirespecialized technology to optimize efficiency). Failure to identify suchproblems may lead to an increase in reduced efficiency and a decline inpatient satisfaction and even revenue.

SUMMARY

This Summary is provided to introduce a selection of concepts in asimplified form that are further described below in the DetailedDescription. This Summary is not intended to identify key features oressential features of the claimed subject matter, nor is it intended tobe used as an aid in determining the scope of the claimed subjectmatter. The present invention is defined by the claims.

In brief and at a high level, this disclosure describes, among otherthings, methods, systems, and computer-readable media for analyzing timespent in, for instance, electronic medical records (EMRs) to identifyone or more of clinicians, practice areas, locations, etc. that arespending an amount of time exceeding a predetermined threshold amount oftime in an EMR. Further analysis may associate a cost with the amount oftime spent in an EMR. The time data may be spliced into one or morecategories or activities to further narrow the analysis. Categories mayinclude, for example, clinician categories, location categories,specialty categories, patient categories, and the like. Thus, time datamay be spliced to show time data for one individual clinician (i.e., aper-clinician time data level), or one area of practice/specialty, orone particular patient, etc. Activities as used herein, refer generallyto one or more actions taken by a user. The actions may be performedwhile in an EMR. Activities include, but are not limited to,documentation, order entry, chart review, patient history, medicationreview/entry, and the like. Such splicing or segmentation provides anexact view into the amount of time spent in EMRs.

BRIEF DESCRIPTION OF THE DRAWINGS

Embodiments are described in detail below with reference to the attacheddrawings figures, wherein:

FIG. 1 is a block diagram of an exemplary computing system suitable toimplement embodiments of the present invention;

FIG. 2 depicts an exemplary chart illustrating a plurality of activitiesperformed while in a plurality of EMRs;

FIG. 3 depicts an exemplary chart illustrating a plurality of activitiesperformed while in a plurality of EMRs further segmented intosub-activities, in accordance with an embodiment of the presentinvention;

FIG. 4 depicts an exemplary clinician comparison summary, in accordancewith an embodiment of the present invention;

FIG. 5 depicts an exemplary per-specialty time data summary, inaccordance with an embodiment of the present invention;

FIG. 6 depicts a per-clinician time data summary, in accordance with anembodiment of the present invention;

FIG. 7 depicts a per-clinician time data summary, in accordance with anembodiment of the present invention;

FIG. 8 depicts an exemplary graphical user interface (GUI) illustratingidentifying time data issues, in accordance with an embodiment of thepresent invention;

FIG. 9 depicts an exemplary recommendation GUI, in accordance with anembodiment of the present invention;

FIG. 10 depicts an assignment GUI, in accordance with an embodiment ofthe present invention;

FIG. 11 depicts a results GUI, in accordance with an embodiment of thepresent invention;

FIG. 12 depicts a per-patient time data summary further segmented to aper-clinician time data level, in accordance with an embodiment of thepresent invention;

FIG. 13 depicts a per-patient time data GUI, in accordance with anembodiment of the present invention;

FIG. 14 depicts an exemplary system architecture suitable forimplementing embodiments of the present invention;

FIG. 15 depicts a flow diagram of an exemplary algorithm for carryingout embodiments of the present invention;

FIG. 16 depicts a flow diagram of an exemplary algorithm for carryingout embodiments of the present invention; and

FIG. 17 depicts a flow diagram of an exemplary algorithm for carryingout embodiments of the present invention.

DETAILED DESCRIPTION

The subject matter of the present invention is described withspecificity herein to meet statutory requirements. However, thedescription itself is not intended to limit the scope of this patent.Rather, the inventors have contemplated that the claimed subject mattermight also be embodied in other ways, to include different steps orcombinations of steps similar to the ones described in this document, inconjunction with other present or future technologies. Moreover,although the terms “step” and/or “block” may be used herein to connotedifferent elements of methods employed, the terms should not beinterpreted as implying any particular order among or between varioussteps herein disclosed unless and except when the order of individualsteps is explicitly described.

Embodiments of the present invention are directed to methods, systems,and computer-readable media for analyzing time data. In one aspect, theinvention is directed to analyzing time data for time spent in, forinstance, electronic medical records (EMRs) to identify one or more ofclinicians, practice areas, locations, etc. that are spending an amountof time exceeding a predetermined threshold amount of time in an EMR.Such identification may be helpful to identify problem areas so thatmodifications may be implemented to try and improve the problem areas(e.g., by reducing the amount of time spent in the EMR). Large amountsof time are required to be spent in patient records due, in part, to thecurrent state of technology in healthcare. However, any improvements toefficiency while in the EMR will only improve clinician efficiency,healthcare facility efficiency, and patient satisfaction.

Further embodiments of the invention perform an analysis to associate acost with the amount of time spent in an EMR. Such association mayprovide insight into areas where costs are too high due to, forinstance, inefficiency.

To maximize the analysis of the time data, the time data may be splicedinto one or more categories or activities to further narrow theanalysis. As previously mentioned, categories may include, for example,clinician categories, location categories, specialty categories, patientcategories, and the like. Thus, time data may be spliced to show timedata for one individual clinician (i.e., a per-clinician time datalevel), one area of practice/specialty, one particular location, or oneparticular patient, etc. Activities as used herein, refer generally toone or more actions taken by a user. The actions may be performed whilein an EMR. Activities include, but are not limited to, documentation,order entry, chart review, patient history, medication review/entry, andthe like. Such splicing or segmentation provides an exact view into theamount of time spent in EMRs.

A first aspect is directed to a computerized method, carried out by atleast one server having one or more processors. The method includes, inpart, receiving time data, wherein the time data represents a totalamount of time spent in one or more electronic medical records (EMRs) bya plurality of clinicians; segmenting the time data to a per-cliniciantime data level such that the time data illustrates the total amount oftime spent in the one or more EMRs by each clinician individually;segmenting the per-clinician time data such that the per-clinician timedata illustrates one or more activities performed by each clinicianindividually while in the one or more EMRs; identifying one or moreclinicians associated with a total amount of time spent in the one ormore EMRs that exceeds a predetermined threshold amount of time; andidentifying one or more recommendations to modify the total amount oftime spent in the one or more EMRs by the one or more cliniciansassociated with the total amount of time spent in the one or more EMRsexceeding the predetermined threshold amount of time.

A second aspect is directed to one or more computer storage media havingcomputer-executable instructions embodied thereon that, when executed,facilitate a method of time data analysis. The media includes receivingtime data for a plurality of patients in a facility, wherein the timedata represents a total amount of time spent in one or more electronicmedical records (EMRs) by one or more clinicians; segmenting the timedata to a per-patient time data level such that the time dataillustrates the total amount of time spent in each patient's EMRindividually; identifying one or more patients associated with a totalamount of time spent in their respective EMR that exceeds apredetermined threshold amount of time; and identifying one or morerecommendations to modify the total amount of time spent in eachpatient's EMR of the one or more patients associated with the totalamount of time spent in their respective EMRs that exceeds thepredetermined threshold amount of time.

A third aspect is directed to one or more computer-readable media havingcomputer-executable instructions embodied thereon that, when executed,facilitate a method of time data analysis. The method includes receivingtime data for a facility representing a total amount of time spent inone or more electronic medical records (EMRs) by a plurality ofclinicians; segmenting the time data to a per-clinician time data levelsuch that the time data illustrates the total amount of time spent inthe one or more EMRs by one or more clinicians; normalizing the timedata to identify a normalized time data, wherein the time data isnormalized by dividing the total amount of time spent in the one or moreEMRs for each clinician of the one or more clinicians by a total numberof patients seen by each respective clinician; identifying a systemadoption rate for each clinician, wherein the system adoption rate is apercent value representing a percent of work being done by the clinicianin the EMR; using the system adoption rate for each clinician, adjustingthe normalized time data for each clinician, wherein the adjustingcomprises: (1) comparing the system adoption rate for each clinician toa 100% system adoption rate, (2) upon identifying the system adoptionrate is lower than 100% system adoption rate, identifying an adjustedtime data value for each clinician to adjust the system adoption rate to100%, and (3) increasing the normalized time data for each clinician totheir respective adjusted time data value; using the adjusting time datavalue for each clinician, identifying one or more clinicians associatedwith an adjusted time data value exceeding a predetermined thresholdamount of time; and identifying one or more recommendations to modifythe adjusted time data value for each clinician associated with theadjusted time data value exceeding a predetermined threshold amount oftime.

Referring to the drawings in general, and initially to FIG. 1 inparticular, an exemplary computing system environment, for instance, amedical information computing system, on which embodiments of thepresent invention may be implemented is illustrated and designatedgenerally as reference numeral 100. It will be understood andappreciated by those of ordinary skill in the art that the illustratedmedical information computing system environment 100 is merely anexample of one suitable computing environment and is not intended tosuggest any limitation as to the scope of use or functionality of theinvention. Neither should the medical information computing systemenvironment 100 be interpreted as having any dependency or requirementrelating to any single component or combination of componentsillustrated therein.

The present invention may be operational with numerous other generalpurpose or special purpose computing system environments orconfigurations. Examples of well-known computing systems, environments,and/or configurations that may be suitable for use with the presentinvention include, by way of example only, personal computers, servercomputers, hand-held or laptop devices, multiprocessor systems,microprocessor-based systems, set top boxes, programmable consumerelectronics, network PCs, minicomputers, mainframe computers,distributed computing environments that include any of theabove-mentioned systems or devices, and the like.

The present invention may be described in the general context ofcomputer-executable instructions, such as program modules, beingexecuted by a computer. Generally, program modules include, but are notlimited to, routines, programs, objects, components, and data structuresthat perform particular tasks or implement particular abstract datatypes. The present invention may also be practiced in distributedcomputing environments where tasks are performed by remote processingdevices that are linked through a communications network. In adistributed computing environment, program modules may be located inlocal and/or remote computer storage media including, by way of exampleonly, memory storage devices.

With continued reference to FIG. 1, the exemplary medical informationcomputing system environment 100 includes a general purpose computingdevice in the form of a server 102. Components of the server 102 mayinclude, without limitation, a processing unit, internal system memory,and a suitable system bus for coupling various system components,including database cluster 104, with the server 102. The system bus maybe any of several types of bus structures, including a memory bus ormemory controller, a peripheral bus, and a local bus, using any of avariety of bus architectures. By way of example, and not limitation,such architectures include Industry Standard Architecture (ISA) bus,Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA) bus, VideoElectronic Standards Association (VESA) local bus, and PeripheralComponent Interconnect (PCI) bus, also known as Mezzanine bus.

The server 102 typically includes, or has access to, a variety ofcomputer readable media, for instance, database cluster 104.Computer-readable media can be any available media that may be accessedby server 102, and includes volatile and nonvolatile media, as well asremovable and non-removable media. By way of example, and notlimitation, computer readable media may include computer storage mediaand communication media. Computer storage media may include, withoutlimitation, volatile and nonvolatile media, as well as removable andnon-removable media implemented in any method or technology for storageof information, such as computer-readable instructions, data structures,program modules, or other data. In this regard, computer storage mediamay include, but is not limited to, RAM, ROM, EEPROM, flash memory orother memory technology, CD-ROM, digital versatile disks (DVDs) or otheroptical disk storage, magnetic cassettes, magnetic tape, magnetic diskstorage, or other magnetic storage device, or any other medium which canbe used to store the desired information and which may be accessed bythe server 102. Computer storage media does not comprise signals per se.Communication media typically embodies computer-readable instructions,data structures, program modules, or other data in a modulated datasignal, such as a carrier wave or other transport mechanism, and mayinclude any information delivery media. As used herein, the term“modulated data signal” refers to a signal that has one or more of itsattributes set or changed in such a manner as to encode information inthe signal. By way of example, and not limitation, communication mediaincludes wired media such as a wired network or direct-wired connection,and wireless media such as acoustic, RF, infrared, and other wirelessmedia. Combinations of any of the above also may be included within thescope of computer-readable media.

The computer storage media discussed above and illustrated in FIG. 1,including database cluster 104, provide storage of computer-readableinstructions, data structures, program modules, and other data for theserver 102.

The server 102 may operate in a computer network 106 using logicalconnections to one or more remote computers 108. Remote computers 108may be located at a variety of locations in a medical or researchenvironment, for example, but not limited to, clinical laboratories,hospitals and other inpatient settings, veterinary environments,ambulatory settings, medical billing and financial offices, hospitaladministration settings, home health-care environments, and clinicians'offices. Clinicians may include, but are not limited to, a treatingphysician or physicians, specialists such as surgeons, radiologists,cardiologists, and oncologists, emergency medical technicians,physicians' assistants, nurse practitioners, nurses, nurses' aides,pharmacists, dieticians, microbiologists, laboratory experts, geneticcounselors, researchers, veterinarians, students, and the like. Theremote computers 108 may also be physically located in non-traditionalmedical care environments so that the entire health care community maybe capable of integration on the network. The remote computers 108 maybe personal computers, servers, routers, network PCs, peer devices,other common network nodes, or the like, and may include some or all ofthe components described above in relation to the server 102. Thedevices can be personal digital assistants or other like devices.

Exemplary computer networks 106 may include, without limitation, localarea networks (LANs) and/or wide area networks (WANs). Such networkingenvironments are commonplace in offices, enterprise-wide computernetworks, intranets, and the Internet. When utilized in a WAN networkingenvironment, the server 102 may include a modem or other means forestablishing communications over the WAN, such as the Internet. In anetworked environment, program modules or portions thereof may be storedin the server 102, in the database cluster 104, or on any of the remotecomputers 108. For example, and not by way of limitation, variousapplication programs may reside on the memory associated with any one ormore of the remote computers 108. It will be appreciated by those ofordinary skill in the art that the network connections shown areexemplary and other means of establishing a communications link betweenthe computers (e.g., server 102 and remote computers 108) may beutilized.

In operation, a user may enter commands and information into the server102 or convey the commands and information to the server 102 via one ormore of the remote computers 108 through input devices, such as akeyboard, a pointing device (commonly referred to as a mouse), atrackball, or a touch pad. Other input devices may include, withoutlimitation, microphones, satellite dishes, scanners, or the like.Commands and information may also be sent directly from a remotehealthcare device to the server 102. In addition to a monitor, theserver 102 and/or remote computers 108 may include other peripheraloutput devices, such as speakers and a printer.

Although many other internal components of the server 102 and the remotecomputers 108 are not shown, those of ordinary skill in the art willappreciate that such components and their interconnection are wellknown. Accordingly, additional details concerning the internalconstruction of the server 102 and the remote computers 108 are notfurther disclosed herein.

Turning now to FIG. 14, exemplary system architecture 1400 suitable forimplementing embodiments of the present invention is illustrated. Itshould be understood that this and other arrangements described hereinare set forth only as examples. Other arrangements and elements (e.g.,machines, interfaces, functions, orders, and groupings of functions,etc.) can be used in addition to or instead of those shown, and someelements may be omitted altogether. Further, many of the elementsdescribed herein are functional entities that may be implemented asdiscrete or distributed components or in conjunction with othercomponents, and in any suitable combination and location. Variousfunctions described herein as being performed by one or more entitiesmay be carried out by hardware, firmware, and/or software. For instance,various functions may be carried out by a processor executinginstructions stored in memory.

Among other components not shown (including a network, a data base, anda user device, among others), the system 1400 may include a time dataengine 1401. The time data engine 1401 may be configured to perform timedata analysis. The time data analysis may be performed on any time datareceived/retrieved by the time data engine 1401. In embodiments, thetime data is time data received/retrieved from one or more EMRs. Thetime data may be received or retrieved from any other source besides anEMR including, but not limited to, a database or any other item to whichtime data may be tracked. The time data engine 1401 may include areceiving component 1402, an analysis component 1404, a segmentingcomponent 1406, an identifying component 1408, a predicting component1410, and a communicating component 1410.

The time data engine 1401 may further include timing components (notshown) that monitor time spent in EMRs. For instance, the timingcomponents can distinguish between active time in the EMR and inactivetime. Active time, as used herein, refers generally to time spentperforming one or more activities and having an active indication nomore than a predetermined time interval away from another activeindication. Active indications include, but are not limited to,keystrokes (e.g., typing in a keyboard or typing on a touch screen),clicks (e.g., by a mouse), mouse miles (e.g., movement of the mouse),touch screen selections (e.g., any selection of a touch screen), and thelike. Thus, a first active indication can be no more than apredetermined time interval away from a second active indication inorder to have the time between counted as active time. The timingcomponents may monitor the time intervals and compare to a predeterminedtime interval to classify time as active or inactive. The timingcomponent may also include metadata tags for each active indicationassociated with various information including a time stamp for eachactivity, active indications for each activity, and the like. Themetadata tags may also include category tags (indicating a categorydescription) or activity tags (indicating an activity description). Forinstance, a category tag may identify a particular clinician or area ofspecialty associated with the active indication. An activity tag mayidentify the activity performed such as order entry.

The receiving component 1402 may be configured for, among other things,receiving (or retrieving) time data. Time data, as used herein, refersgenerally to a set of data representing a total amount of time spent inone or more EMRs. The total amount of time may be a numericalindication. The time data may be gathered for one or more locations andinclude multiple categories including, but not limited to, one or morepatients, one or more clinicians, one or more areas of practice, and thelike. For instance, FIG. 2 provides an exemplary view of a time datasummary 200 illustrating how clinicians spend their time. This examplewas obtained over a period of one day from 178 different clients andincludes time data for 87,387 clinicians. FIG. 2 illustrates that in theone day studied, clinicians spent almost half of their time in the EMRreviewing charts. Various other activities are highlighted in the timedata summary 200 including documentation, ordering, alerting, and thelike. The activities, as previously stated, include one or more actionsperformed by a user. FIG. 2 also illustrates the capabilities of thetime data engine 1401 as to segmenting time data. In this particulartime data summary 200 the time data, collecting across multiple clientsand multiple clinicians, was compiled and then segmented into aplurality of categories such that an overall view is provided (asopposed to a view regarding a single location or a single clinician).

FIG. 3 illustrates additional capabilities of the time data engine 1401related to segmenting. FIG. 3 provides a view 300 of a time data summary310 and a segmented time data summary 320. As illustrated, the segmentedtime data summary 320 has been further broken down into additionalactivities. As shown, the chart review activity of the time data summary310 has been broken down into five sub-activities in the segmented timedata summary 320. The time data illustrated in FIG. 3 was collected froma single client (i.e., per-location time data level) and includes 3910clinicians. A view similar to FIG. 3 could be obtained by segmenting thetime data summary 200 of FIG. 2 to a per-location time data level.

Returning now to FIG. 14, the analysis component 1404 is configured for,among other things, analyzing the time data. Said analysis may includeidentifying various categories and activities present within the timedata. For instance, in FIG. 2, the analysis component 1404 may compileall of the time data received (by, for example, the receiving component1402) and identify each category and/or activity present within the timedata such that available segmenting options are available to a user. Forexample, if time data is collected for a practice area that does notinclude a particular activity, segmenting the time data by thatparticular activity is not an option. Thus, the analysis component 1404may sort through and organize time data.

The segmenting component 1406 may be configured for, among other things,segmenting time data into segments based on, for instance, categories,activities, and the like. Categories include clinician categories,patient categories, location categories, specialty categories, and thelike. A clinician category, as used herein, refers generally to acategory of time data that is broken down (segmented) to a per-cliniciantime data level. For example, while FIG. 2 was directed to a multipleclients and multiple clinicians, the same type of summary may be offeredfor a single clinician. In fact, a single clinician included in the timedata summary 200 of FIG. 2 may be identified and that data may besegmented for the identified clinician.

A patient category, as used herein, refers generally to a category oftime data that is broken down (segmented) to a per-patient time datalevel. In this embodiment, time data is collected as it pertains to asingle patient. One or more clinicians may be included in the time data.The patient category provides an opportunity to identify time datatrends for one or more patients or types of patients (e.g., cardiacpatients, maternity patients, etc.). This could, in turn, identifypatients or types of patients that require more time than others, forinstance. Patients, as used herein, refer generally to a uniqueindividual that is seen in a predetermined time period (e.g., a day) andis only counted once in the predetermined time period. For instance, apatient that is hospitalized from Sep. 1, 2014 to Sep. 10, 2014 iscounted as a patient each day (assuming one day is the predeterminedtime period) but is not counted each time within a day that the patientis seen.

A location category, as used herein, refers generally to a category oftime data that is broken down (segmented) to a per-location time datalevel. A location may be a facility (e.g., Hospital A or Hospital B) ora department/area within a facility (e.g., Emergency Department ofHospital A). Locations may also include patient rooms, regions of afacility (e.g., floors 1-10 of a facility), and the like. FIG. 3, asmentioned above, is providing time data for a single location.

The location category may also include time data that is segmented to aper-venue time data level. A venue, as used herein, refers generally todifferent venues of care including, but not limited to, emergency,inpatient, outpatient, etc. The time data may be segmented by the venuein which the care is provided. For instance, an OB/GYN physician maywork in the same facility all day long but would work across multiplevenues of care: emergency (e.g., unscheduled delivery), inpatient (e.g.,scheduled delivery), and outpatient (e.g., follow-up visits).

A specialty category, as used herein, refers generally to a category oftime data that is broken down (segmented) to a per-specialty time datalevel. A specialty, as used herein, refers generally to a clinical areaof practice such as cardiology, obstetrics, pediatrics, radiology, etc.A per-specialty time data level is provided in FIG. 5. As shown, timedata may be broken down per specialty and, in FIG. 5, includes primarycare, physical therapy, pediatrics (peds), preventative medicine,psychology, and radiology. The time data may be broken down by severalcategories and/or activities or a combination thereof. FIG. 5illustrates this as each specialty category is further broken down intoactivities (e.g., chart review, ordering, etc.). This illustrates howtime data may be used to identify time data trends among specialties.This time data indicates that primary care clinicians spend much moretime, on average, in EMRs than radiology clinicians.

In addition to segmenting time data, embodiments of the invention aredirected to comparisons using time data. FIG. 4 illustrates an exemplaryclinician comparison summary 400. FIG. 4 is a comparison of nineclinicians (e.g., physicians) from a single client that share a commonarea of specialty and use a same version of the EMR. The time data isbroken down into a per-clinician time data level to illustrate thecomparison of how long it takes on average for a physician to interactwith the EMR per patient seen. It could be broken down into any otherappropriate category/activity. For instance, time data could be brokendown by activity, such as documentation or ordering, so that it can beviewed what is done most often in the patient's EMR (by one or moreclinicians). In FIG. 4, it is clearly seen that Dr. Nine takes much moretime interacting with the EMR than does, for example, Dr. One. This maybe used as a tool to further review the activity of Dr. Nine or toidentify certain patient features or EMR-related features that Dr. Ninemay be struggling with. Ultimately, FIG. 4 illustrates a comparisonembodiment of the present invention where the system can compare howlong it takes clinicians to interact with an EMR on a normalized timeper patient basis (i.e., total time divided by total patients).

In an embodiment, the system divides the active time spent in the EMRper clinician by the number of patients seen. This calculation allowscomparison of clinicians with varying patient volumes to identify, onaverage, how much time clinicians are taking per patient. This conceptcan be extended to account for varying levels of adoption of system usefor two key categories: ordering and documentation. Physicians will,often times, have their nurse or other person place orders or documenton their behalf. In such cases, the physician logs less time in the EMRbecause someone else is doing the work for them in the EMR. In thosecases, the system may adjust the appropriate category of time to accountfor the lower use of the system. An algorithm is used by the system totake into account a clinician's current time ordering and/or documentingand adjusts the time up based on how much they did that particularfunction. This metric may be referred to as “adoption-adjusted time inEMR per patient” and it may be used to identify outliers when comparingclinicians within a given specialty.

As an example, assume Clinician A is only documenting in the system21.57% of the time at 2 minutes and 50 seconds and is only ordering20.85% of the time at 41 seconds. Clinician A's time may be written upfrom 2 minutes and 50 seconds to 6 minutes and 18 seconds to estimatetime documenting at 100%. Similarly, the ordering time may be written upfrom 41 seconds to 1 minute and 32 seconds to estimate time ordering at100%. At this point, Clinician A may be compared to any other cliniciansince the system has accounted for differences in patient volume andhis/her adoption of the system's functionality.

Returning now to FIG. 14, the identifying component 1408 may beconfigured for, among other things, identifying one or more time dataissues. A time data issue, as used herein, refers generally to acategory or an activity that is associated with an amount of time in anEMR that exceeds a predetermined amount of time or a recommended amountof time. For example, clinicians that are associated with an amount oftime in an EMR that exceeds a predetermined amount of time may beidentified as a time data issue. Time data issues may also be patients,locations, areas of specialties, documentation, order entry, and thelike. As a further example, an area of specialty (e.g., radiology) maybe identified as spending an amount of time exceeding a recommendedamount of time in an EMR. The identified time data issues may be sent toa user in, for an example, an alert, a notification, or the like. Thenotification may be in the form of an e-mail message, a text message, anotification indicator in a user interface, or the like.

As described above with reference to FIG. 4, Dr. Nine was previouslyidentified as a time data issue (as Dr. Nine was spending much more timein the patient's EMR than other clinicians). FIG. 6 illustrates afurther segmented view 600 for only Dr. Nine. This view 600 includes anindication of total active time, a total number of patients seen, and atotal time in EMR per patient. This information is further divided intoactivities (e.g., chart review, orders, documentation, etc.) and placedover the hours of the day (x axis). In addition to Dr. Nine spending toomuch time in EMRs, this view 600 further illustrates that Dr. Nine isalso spending time outside of an assigned shift in EMRs. Portion 620indicates the hours Dr. Nine is on-duty while portion 630 indicates thehours Dr. Nine is off-duty. It may not be desirable for clinicians orclients to spend excessive amounts of time in EMRs after expiration of ashift. Thus, the view 600 indicates that not only is Dr. Nine spendingtoo much time in EMRs but Dr. Nine is not efficient enough to doeverything required within a predetermined period of time (i.e., a workshift).

FIG. 7 is provided to show a comparison of clinicians as this is ananalysis of Dr. Five. The view 700 provides a further segmented view 700for only Dr. Five. This view 700 includes an indication of total activetime, a total number of patients seen, and a total time in EMR perpatient. This information is further divided into activities (e.g.,chart review, orders, documentation, etc.) and placed over the hours ofthe day (x axis). This view 700 illustrates that Dr. Five is veryefficient as the time in EMR directly corresponds to the number ofpatients that are checked in there is minimal time spent in EMRs afterthe predetermined time period as indicated by portion 720 (the workshift) and portion 730 (the off duty time).

This data could be compiled in various formats and presented to a userdifferently. For instance, data could be compiled for the same set ofclinicians illustrated in FIG. 4 but focused on the amount of time spentin EMRs outside of business hours. The below table illustrates datacompiled for nine clinicians of a family medicine practice (i.e., singleclient) over the course of five business days. As illustrated below, Dr.Nine has a very high percentage of time spent in EMRs that is outside ofbusiness hours. Dr. Nine may be identified as a time data issue based onthe below time data and one or more recommendations may be provided toimprove performance of Dr. Nine.

TABLE 1 USER % OUTSIDE HOURS Dr. Nine 26.1% Dr. Eight 24.9% Dr. Four13.2% Dr. Seven 11.2% Dr. Six 4.3% Dr. One 3.7% Dr. Two 2.2% Dr. Five0.6% Dr. Three 0.0%

Once the identifying component 1408 has identified one or more time dataissues, the time data issues may be communicated to a user (via thecommunicating component 1412) and displayed. The results or time dataissues may be presented to a user in a graphical user interface (GUI)customized for time data analysis. An exemplary user interface 800 isprovided in FIG. 8. FIG. 8 includes a time data issue indicator 802, arecommendation indicator 804, and a results indicator 806. The time dataissue indicator 802 is configured such that selection thereof navigatesa user to a GUI, similar to that illustrated in FIG. 8, indicating oneor more time data issues. The exemplary GUI 800 further includes asearch area 810 where a user may search for a specific clinician as theyrelate to time data, a location area 812 including an indication of alocation. The area may also include a breakdown of the segments includedin the time data. For instance, GUI 800 illustrates the current view iscustomized to a per-location time data level and then a per-specialtytime data level (as indicated by the “Health System View>Cross-SpecialtyView” navigation indicator). The GUI 800 may further include a time dataresults area 814 that displays at least any time data issues identified.The time data results area 814 may also include those categories oractivities that performed well in the time data analysis. That is, thosecategories or activities associated with an amount of time less than apredetermined amount of time are said to perform well. An expansionindicator 816 may also be featured and configured such that selectionthereof expands the view of the time data results area 814. A time dataissues area 818 is further included and illustrates one or more timedata issues. The time data issues area 818 of FIG. 8 is segmented bypractice area and then by clinician. For instance, the time data issuesarea 818 indicates that 51/93 physicians in the internal medical grouprequire attention. The time data issues displayed in the time dataissues area 818 of FIG. 8 are those that are associated with a highlevel of attention required. Further time data issues may be viewed byselecting a moderate indicator or a low indicator.

Once time data issues are identified, the identifying component 1408 maybe further configured to identify one or more modifications orrecommendations to modify or improve the time data issue. For instance,if a time data issue is a clinician spending too much time in EMRs, theidentifying component 1408 may identify one or more recommendations toimprove the clinician's performance such as specialized training. Suchrecommendations may be presented in a GUI such as GUI 900 of FIG. 9. Asillustrated in GUI 900, the time data issue indicator 904 and theresults indicator 906 are present as well as the recommendationindicator 902 (currently selected).

The recommendation indicator 902 is configured such that selectionthereof navigates a user to a GUI similar to that of FIG. 9 thatindicates one or more recommendations. The GUI 900 includes arecommendation summary area 910 that includes one or morerecommendations. Recommendations may be broken down into severalcategories such as innovation, configuration, training, and the like.Innovation recommendations may be described as recommendations toimplement a new action including, but not limited to, a new code orsoftware. For example, if a particular practice group is exceeding apredetermined amount of time and is identified as a time data issue, theidentifying component 1408 may identify that the practice group does nothave a particular piece of system functionality installed and recommendsinstallation (of a new, not previously installed, piece offunctionality).

In contrast, configuration recommendations may be described asmodifications to existing technology. For instance, in the aboveexample, assume that the practice area does have the systemfunctionality installed but they need a specialized view to optimizeefficiency. This would be a modification to existing technology andcategorized as a configuration recommendation.

Training recommendations include any training sessions that may berecommended including educational information associated with the timedata issue or improvement thereof. Training may be recommended based ona comparison of time data with predefined thresholds included intraining recommendations or comparison to other users of the samespecialty category. The predefined thresholds may be user defined andcustomized for unique needs.

The GUI includes an innovations indicator 910A, a configurationindicator 910B, and a training indicator 910C. Selection of any one ofthe innovations indicator 910A, the configuration indicator 910B, or thetraining indicator 910C may result in a display of one or morerecommendations in that category. Alternative, and as illustrated inFIG. 9, a summary of one or more recommendations for each category maybe included in proximity to the respective indicator. For example,“create a patient list” is listed as a potential training recommendationnear the training indicator 910C.

The GUI 900 further includes an opportunity area 914 illustrating one ormore categories for which opportunities or recommendations exist. Theopportunity area 914 includes filter area 916 including a specialtyindicator 916A (currently selected), a facility indicator 916B, and auser indicator 916C. Selection of any one of the specialty indicator916A (currently selected), the facility indicator 916B, or the userindicator 916C may result in a filtered view of opportunities associatedwith the selected filter. For example, in FIG. 9, the specialtyindicator 916A is selected so the opportunity area 914 displays aplurality of specialty groups (e.g., internal medicine, pediatrics,orthopaedics, neurology, podiatry, etc.) and opportunities associatedtherewith. For instance, with respect to internal medicine, a number ofphysicians (either total included in time data or a number that areidentified as time data issues) is displayed as well as opportunitiesdivided by category such that all opportunities (total of 36) aredisplayed as well as innovation opportunities (8), configurationopportunities (12), and training opportunities (16) being individuallydisplayed.

Upon displaying the one or more recommendations, one or morerecommendations may be assigned to, for example, a clinician, afacility, a specialty group, etc. FIG. 10 provides an exemplary GUI 1000illustrating assigning a training recommendation to a clinician. Thetime data issue indicator 1002 is currently selected and a particularclinician 1004 has been selected. An assignment tool 1008 is displayedwhere time data for the clinician 1004 is displayed and one or morerecommended actions are displayed. One of more of the recommendedactions may be selected to be assigned to the clinician 1004. In thisexample, “How to Use a Pre-completed Note” training is selected toassign. Furthermore, a metric goal may be assigned such that progress ismonitored. Once the recommendation is selected and a goal is set, a usermay select an assign indicator 1006 to assign the recommendations to theclinician 1004. Additional feedback may be monitored such assatisfaction level, receptivity (to training), etc. A notes area 1010may also be presented with a follow up indicator 1012 indicating whenfollow up should be performed. Upon completion a save indicator 1014 maybe selected to save the assignment.

The satisfaction and receptivity indicators may be valuable in trackingengagement with clinicians throughout training. This may facilitatetraining coverage (via notes sections), physician sentiment andwillingness to engage. This feedback may be useful in customizing futuretraining sessions.

An exemplary implementation is illustrated in FIG. 11. FIG. 11 providesan exemplary GUI 1100 that includes a results indicator 1110 (currentlyselected) and a filter area 1120 including one or more filters by whichthe time data has been filtered. In this illustration, the filter hasbeen set to a specific location or venue of care—ambulatory. An eventsarea 1130 includes an events legend 1135 indicating one or more events(recommendations) that are to be implemented. For example, event 1139 isa configuration event while event 1137 is a training event. Indicators1140, 1141, and 1142 indicate an amount of time in EMRs relative toevent 1139, the event that has been selected for evaluations. Forexample, indicator 1140 is a measure of the amount of time in EMRs priorto event 1139 while indicator 1141 is a measure of time spent in EMRsafter the configuration event 1139. As may have been intended, theamount of time spent in EMRs decreased as the configuration event 1139was implemented. This could potentially be matched up with a predictedimprovements area (not shown). Once recommendations are made andimplemented, the predicting component 1410 may identify projectedimprovements. For instance, based on the implemented recommendation, thepredicting component 1410 may identify a measurable improvement such asa reduction in an amount of time spent in EMRs. By overlaying thepredicted results with the actual results of FIG. 11, it may bebeneficial to identify other issues, additional opportunities (e.g.,more training sessions may have been beneficial), etc. When result datais received, it may be compared to predicted improvements and, based onthe comparison, additional modifications may be required if the updatedresult data is still exceeding a predetermined amount of time in an EMR.

The results of FIG. 11 (and the implementation of modifications) may beillustrated in a results area 1150. As is shown, the EMR time changeindicator 1151 indicates a reduction in the amount of time spent in EMRsby 21.7%.

Turning now to FIG. 12, an exemplary GUI 1200 is provided illustrating atime data analysis for a single patient (per-patient time data level)that is further broken down by clinician-type (e.g., nurse, mid-levelclinician, therapist, emergency department physician, orthopaedicphysician, internal medicine physician, etc.). This is a patient-centricview that illustrates how much time each type of clinician spent in theEMR for this patient. This data may be used to, among other things,identify time data issues (e.g., if a particular clinician orclinician-type is spending too much time in the EMR), identify use(e.g., which clinician-types use the EMR the most through the day sothat future innovations/training/configurations may be directed to theuse), and identify costs for types of patients. As is noted in FIG. 12,the patient analyzed in this example is a hip fracture patient. All ofthe time spent in the EMR could be converted to cost (e.g., U.S.dollars) to identify potential time data issues (e.g., times spent inthe EMR that are associated with a cost exceeding a predetermined costthreshold). In those situations it may be an issue with how much time isspent in an EMR or it could be identified that rather than EMRmodifications, a particular clinician would benefit more from delegationeducation and handing off some tasks that could be done by otherclinicians. For instance, if a physician is spending large amounts oftime in an EMR reviewing something that a nurse or other level cliniciancould handle, the physician may need to pass some of those duties on toincrease their own efficiency and reduce cost.

Turning now to FIG. 13, an exemplary GUI 1300 is provided thatillustrates a per-patient view. The GUI 1300 includes a patientidentification area 1302 and a patient information area 1304 that mayinclude a diagnosis, any conditions of the patient, length of stayinformation, and the like. The GUI 1300 may further include an EMRsummary area 1306 that includes a total time in the patient's EMR. TheEMR summary area 1306 may include additional information including alocal comparison that compares the time spent in the EMR with localfacilities and a cost associated with the time spent in the EMR and,also, a peer comparison that compares time spent in the EMR with one ormore peers of the facility at which the patient is present. A peer maybe a facility that is similar in nature to that which the patient isadmitted. For instance, an academic medical center would be a peer ofother academic medical centers and not likely a peer of a criticalaccess hospital. The information in the EMR summary area 1306 may alsobe presented in a graphical EMR summary area 1308, which includes theinformation of the EMR summary area 1306 but in graphical form insteadof textual form. The time data of the EMR summary area 1306 may befurther spliced by one or more categories and/or activities. The timedata segment area 1310 illustrates said spliced time data. In thisexample, the time data segment area 1310 includes the time datasegmented by clinician-type. For instance, the time data is segmented byphysician, mid-level clinician, nurse, therapist, etc. This provides atool to readily identify clinicians that may be spending too much timein the patient's EMR.

Once collected and analyzed, the communicating component 1412 may beconfigured for, among other things, communicating one or more of timedata, time data analysis results, time data segmentation, one or morerecommendations, and the like to a user.

In further embodiments, the system may use time data to predict pathwaysof a clinician. A pathway, as used herein, refers generally to a paththrough an EMR or where a user went or goes in an EMR. This pathwayprediction may be formulated using a set of time data collected over apredetermined period of time for a clinician. For example, assume timedata is collected for Clinician A over a period of one week. Using thatdata, the system 1400 may identify (using, for example, the identifyingcomponent 1408) that Clinician A opens an EMR and goes to an OrderSection (for inputting orders) 97% of the time. Using this data, apredicted pathway may be created whereby it is predicted that ClinicianA will go directly to the Order Section upon opening the EMR. Inembodiments, this predicted pathway may be used to customize ClinicianA's experience such that when Clinician A opens an EMR the Order Sectionwill be immediately presented so that Clinician A will not have tonavigate to the Order Section. Thus, the Order Section, in this example,may be automatically displayed. Predicted pathways may be useful toincrease efficiency. As illustrated in the previous example, an entirestep was eliminated for Clinician A when the Order Section isautomatically populated. If this is used by the Clinician A 97% of thetime (i.e., the 97% of the time when Clinician A desires to go directlyto the Order Section) then Clinician A will have saved time throughoutthe day.

FIG. 15 depicts a flow diagram of a method 1500 for time data analysis,in accordance with an embodiment of the present invention. At block1502, time data is received. Additional data relevant to time dataanalysis such as the number of patients seen per clinician may also bereceived. Time data may represent a total amount of time spent in one ormore EMRs by one or more clinicians. At block 1504, the time data issegmented to a per-clinician time data level such that the time dataillustrates the total amount of time spent in the one or more EMRs byeach clinician individually. The data may be normalized by the number ofpatients seen to facilitate comparisons between clinicians withdifferent patient volumes. An additional step may be applied to accountfor varying levels of system functionality used by different clinicians.For example, if Physician A only places 25% of his orders using systemfunctionality, his Orders time in EMR per patient will likely be lowerthan another provider that places 75% of his orders using the systemfunctionality. To account for this different in adoption of systemfunctionality, the time data may be adjusted to estimate the total timespent in the EMR if the provider were to use the system functionality at100%. This adjusted time in EMR per patient allows for a comparison ofany two clinicians in the system no matter their patient volume or levelof adoption of system functionality.

Continuing on with FIG. 15, at block 1506, the per-clinician time datais segmented such that the per-clinician time data illustrates one ormore activities performed by each clinician individually while in theone or more EMRs. At block 1508, one or more clinicians associated witha total amount of time spent in the one or more EMRs exceeding apredetermined threshold amount of time is identified. At block 1510, oneor more recommendations to modify the total amount of time spent in theone or more EMRs by the one or more clinicians associated with the totalamount of time spent in the one or more EMRs exceeding the predeterminedthreshold amount of time is identified.

FIG. 16 depicts a flow diagram of another method 1600 for time dataanalysis, in accordance with an embodiment of the present invention.Initially, at block 1602, time data for a plurality of patients in afacility is received. The time data may represent a total amount of timespent in one or more EMRs by one or more clinicians. At block 1604, thetime data is segmented to a per-patient time data level such that thetime data illustrates the total amount of time spent in each patient'sEMR individually. At block 1606, one or more patients associated with atotal amount of time spent in their EMR that exceeds a predeterminedthreshold amount of time is identified. At block 1608, one or morerecommendations to modify the total amount of time spent in eachpatient's EMR is identified.

FIG. 17 depicts a flow diagram of another method 1700 for time dataanalysis, in accordance with an embodiment of the present invention.Initially, at block 1702, time data for a facility is received where thetime data represents a total amount of time spent in one or more EMRs bya plurality of clinicians. At block 1704, the time data is segmented toa per-clinician time data level such that the time data illustrates thetotal amount of time spent in the one or more EMRs by one or moreclinicians. At block 1706, the time data is normalized to identifynormalized time data for each clinician. The time data may be normalizedby dividing the total amount of time spent in the one or more EMRs foreach clinician of the one or more clinicians by a total number ofpatients seen by each respective clinician. At block 1708, a systemadoption rate is identified for each clinician. A system adoption raterepresents a percent of work being done by the clinician in the EMRindicating, generally, how often the clinician uses the system foractivities (such as documentation, ordering, etc.) versus one or morealternatives such as the work being done by another user or being doneoutside of the system. The system adoption rate is typically a percentvalue (e.g., 98%, 22.3%, etc.). At block 1810, the normalized time datais adjusted for each clinician. The adjustment step comprises: comparingthe system adoption rate for each clinician to a 100% system adoptionrate; upon identifying the system adoption rate is lower than 100%system adoption rate, identifying an adjusted time data value for eachclinician to adjust the system adoption rate to 100%; and increasing thenormalized time data for each clinician to their respective adjustedtime data value. As an example, system adoption rates may be lower forclinicians that delegate their EMR duties (e.g., documenting, ordering,etc.) to other clinicians (e.g., nurses, etc.). Those clinicians thatdelegate, naturally, have a lower percentage of time spent in the EMRthan clinicians that access the EMR themselves. At block 1712, one ormore clinicians associated with an adjusted time data value exceeding apredetermined threshold amount of time is identified. At block 1714, oneor more recommendations to modify the adjusted time data value for eachclinician associated with the adjusted time data value exceeding apredetermined threshold amount of time is identified.

The present invention has been described in relation to particularembodiments, which are intended in all respects to be illustrativerather than restrictive. Further, the present invention is not limitedto these embodiments, but variations and modifications may be madewithout departing from the scope of the present invention.

What is claimed is:
 1. A computerized method, carried out by at leastone server having one or more processors, the method comprising:receiving per-clinician time data, wherein the per-clinician time datarepresents a total amount of time spent in one or more EMRs by aclinician; segmenting the per-clinician time data such that theper-clinician time data represents one or more activities performed bythe clinician while in the one or more EMRs; determine that the totalamount of time spent in the one or more EMRs exceeds a predeterminedthreshold amount of time; and utilizing the per-clinician time data,creating a predicted pathway for the clinician, the predicted pathwayautomatically redirecting a default starting view associated withopening the one or more EMRs from a first view to a second view withouthaving to navigate from the first view to the second view, wherein theclinician has navigated from the first view of the one or more EMRs to asecond view of the one or more EMRs a number of times greater than apredetermined threshold.
 2. The method of claim 1, wherein the totalamount of time spent in the one or more EMRs represents active time,wherein active time includes time spent performing one or moreactivities associated with an active indication having a time intervalbetween a subsequent active indication that is no greater than apredefined time interval.
 3. The method of claim 2, wherein the activeindication includes one or more of a keystroke, a mouse click, or mousemiles.
 4. The method of claim 2, wherein the active time isdistinguished from inactive time using one or more timers for measuringa time interval between two or more active indications.
 5. The method ofclaim 4, wherein the one or more timers collect metadata including atime stamp for an activity, a description of the activity, and one ormore tags for data segmentation.
 6. The method of claim 5, wherein theper-clinician time data is segmented based on one or more of area ofspecialty, location, clinician, patient, or activity.
 7. The method ofclaim 5, wherein the one or more tags include a clinician tag, a patienttag, a location tag, and an area of specialty tag.
 8. The method ofclaim 1, wherein the per-clinician time data is collected over apredetermined time period.
 9. The method of claim 8, wherein thepredetermined time period is one day.
 10. The method of claim 1, furthercomprising identifying a portion of the per-clinician time data is thatcollected from one or more clinicians during outside hours, whereinoutside hours are outside of a designated shift of hours.
 11. One ormore computer storage media having computer-executable instructionsembodied thereon that, when executed, facilitate a method of time dataanalysis, the method comprising: receiving per-clinician time data,wherein the per-clinician time data represents a total amount of timespent in one or more electronic medical records (EMRs) by a clinician;segmenting the per-clinician time data such that the per-clinician timedata represents one or more activities performed by the clinician whilein the one or more EMRs; determine that a total amount of time spent inthe one or more EMRs exceeds a predetermined threshold amount of time;and utilizing the per-clinician time data, creating a predicted pathwayfor the clinician, the predicted pathway automatically redirecting adefault starting view associated with opening the one or more EMRs froma first view to a second view without having to navigate from the firstview to the second view, wherein the clinician has navigated from thefirst view of the one or more EMRs to a second view of the one or moreEMRs a number of times greater than a predetermined threshold.
 12. Themedia of claim 11, wherein the per-clinician time data is collected fora predetermined time period.
 13. The media of claim 11, furthercomprising segmenting the per-clinician time data into one or moreactivities performed in each patient's EMR on a per-patient basis. 14.The media of claim 13, further comprising associating each activity ofthe one or more activities performed in each patient's EMR with a cost.15. The media of claim 11, further comprising associating each patientwith a cost based on the total amount of time spent in their respectiveEMRs exceeding the predetermined threshold.
 16. A system forfacilitating time data analysis, the system comprising: a processorconfigured to: receive per-clinician time data, wherein theper-clinician time data represents a total amount of time spent in oneor more Electronic Medical Records (EMRs) by a clinician; segment theper-clinician time data such that the per-clinician time data representsone or more activities performed by the clinician while in the one ormore EMRs; determine that the total amount of time spent in the one ormore EMRs exceeds a predetermined threshold amount of time; and utilizethe per-clinician time, creating a predicted pathway for the clinician,the predicted pathway automatically redirecting a default starting viewassociated with opening the one or more EMRs from a first view to asecond view without having to navigate from the first view to the secondview, wherein the clinician has navigated from the first view of the oneor more EMRs to a second view of the one or more EMRs a number of nbbtimes greater than a predetermined threshold.
 17. The system of claim16, wherein the per-clinician time data is collected for a predeterminedtime period.
 18. The system of claim 16, wherein the processor isfurther configured to segment the per-clinician time data into one ormore activities performed in each patient's EMR on a per-patient basis.19. The system of claim 16, wherein the processor is further configuredto: receive updated time data subsequent to the creation of thepredicted pathway; compare the updated time data to the per-cliniciantime data; and based on the comparison, identify one or morerecommendations to modify the total amount of time spent in the one ormore EMRs if the updated time data for the per-clinician time data levelstill exceeds the predetermined threshold amount of time.
 20. The systemof claim 16, further comprising associating each clinician with a cost.